This information is for the 2021/22 session.
Dr Vassilis Hajivassiliou and Ragvir Sabharwal
This course is compulsory on the MRes/PhD in Accounting (EoA) (Economics of Accounting Track) , MSc in Economics and MSc in Economics (2 Year Programme). This course is available on the MPhil/PhD in Environmental Economics, MSc in Economics and Philosophy and MSc in Quantitative Economic History. This course is available with permission as an outside option to students on other programmes where regulations permit.
Students must have completed Introductory Course in Mathematics and Statistics (EC400).
Students should also have completed an undergraduate degree or equivalent in Economics and an introductory course in Econometrics.
In very exceptional circumstances, students may take this course without EC400 provided they meet the necessary requirements and have received approval from the course conveners (via an online* face to face meeting), the MSc Economics Programme Director and their own Programme Director. Contact the Department of Economics for more information (firstname.lastname@example.org).
The course aims to present and illustrate the techniques of empirical investigation in economics.
- Regression models with fixed regressors (simple and multiple). Least squares and other estimation methods. Goodness of fit and hypothesis testing.
- Regression models with stochastic regressors.
- Asymptotic theory and its application to the regression model. Sampling error vectors. Large sample approximations.
- The partitioned regression model, multicollinearity, misspecification, omitted and added variables, measurement errors.
- Heteroskedasticity, autocorrelation, and generalized least squares.
- Exogeneity, endogeneity, and instrumental variables. The leading causes of endogeneity.
- Nonlinear regression modelling and Limited Dependent Variables models.
- An introduction to Non-classical econometric inference.
- Autoregressive and moving average representations of time series. Stationarity and invertibility.
- Ergodicity, Laws of Large Numbers, and Central Limit Theorems for Time Series
- Vector auto-regressions.
- Unit roots and co-integration.
- Estimating causal effects in panel data: differences in difference estimator, matching methods, and regression discontinuity.
- Panel data and static models: fixed and random effect estimators, clustering. specification tests, measurement errors.
- Panel data and dynamic models: generalized method of moments.
- Binary choice models with panel heterogeneity.
30 hours of lectures and 10 hours of classes in the MT. 30 hours of lectures and 10 hours of classes in the LT.
This course is delivered through a combination of classes and lectures totalling a minimum 80 hours across Michaelmas Term and Lent Term. This year, some or all of this teaching will be delivered through a combination of virtual classes, live streamed (recorded) lectures, and some flipped content delivered as short online videos.
Two marked assignments per term. Exercises are provided each week and are discussed in classes. In order to have any chance of completing the course successfully, these exercises must be attempted. Special test exercises will be set at three points during the year. These will be carefully marked and the results made available.
W H Greene, Econometric Analysis (6th edn), James D. Hamilton, Time Series Analysis (1994), J Wooldridge, Econometric Analysis of Cross Section and Panel Data (2002), J Angrist and J Pischke, Mostly Harmless Econometrics (2009)
Exam (50%, duration: 3 hours, reading time: 10 minutes) in the January exam period.
Exam (50%, duration: 2 hours, reading time: 15 minutes) in the summer exam period.
Course selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Important information in response to COVID-19
Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.
Total students 2020/21: 201
Average class size 2020/21: 19
Controlled access 2020/21: Yes
Value: One Unit